In the point cloud domain, PU-Net [1]} introduced a first point upsampling network.
They divide the mesh into small patches and group the point w.r.t. different radii values and extract features.
Subsequently, all features are concatenated and reshaped to yield upscaled point features.
Based on this architecture, they introduced another network that preserves sharp edges called EC-Net [2]}.
There is another network based on GAN [3]}, PU-GAN [4]}.
All these methods directly employ neural network on point cloud. Recent work PUGeo-Net [5]} uses surface parameterization to map 3D surface to 2D parametric domain. They also estimates the normals with upsampled points. In contract to this method, our method is directly employed on point cloud.
